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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.


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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.25.1

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2024-10-23, 19:37 CST based on data in: /scratch/home/dperez/resultados/Pre_data/out


        General Statistics

        Showing 0/30 rows and 13/27 columns.
        Sample Name≥ 1X≥ 5X≥ 10X≥ 30X≥ 50XMedianMean Cov.Min Cov.Max Cov.Mb Total Coverage BasesGenome lengthInsert SizeMean Insert SizeDuplication% Duplication% > Q30Mb Q30 basesReads After FilteringGC content% PF% AdapterDupsGCAvg lenMedian lenFailedSeqs
        H67
        0.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0.0X
        0.0X
        72.0X
        27.6Mb
        3173861256
        H67_S2_L001
        0.0%
        93.6%
        6.5Mb
        0.1M
        46.9%
        91.9%
        0.2%
        H67_S2_L001_R1
        0.1%
        46.0%
        76bp
        76bp
        0%
        0.0M
        H67_S2_L001_R2
        0.1%
        46.0%
        76bp
        76bp
        0%
        0.0M
        H67_S2_L002
        0.0%
        92.8%
        6.5Mb
        0.1M
        46.8%
        91.7%
        0.2%
        H67_S2_L002_R1
        0.1%
        46.0%
        76bp
        76bp
        0%
        0.0M
        H67_S2_L002_R2
        0.2%
        46.0%
        76bp
        76bp
        0%
        0.0M
        H67_S2_L003
        0.0%
        92.7%
        6.5Mb
        0.1M
        46.8%
        92.1%
        0.2%
        H67_S2_L003_R1
        0.1%
        46.0%
        76bp
        76bp
        0%
        0.0M
        H67_S2_L003_R2
        0.1%
        46.0%
        76bp
        76bp
        0%
        0.0M
        H67_S2_L004
        0.0%
        92.4%
        6.4Mb
        0.1M
        46.7%
        91.8%
        0.2%
        H67_S2_L004_R1
        0.1%
        46.0%
        76bp
        76bp
        0%
        0.0M
        H67_S2_L004_R2
        0.1%
        46.0%
        76bp
        76bp
        0%
        0.0M
        H67_merged
        0.1%
        H67_sorted_dedup
        179bp
        186bp
        H82
        0.0%
        0.0%
        0.0%
        0.0%
        0.0%
        0.0X
        0.0X
        33.0X
        14.9Mb
        3171791034
        H82_S1_L001
        0.0%
        94.8%
        7.0Mb
        0.1M
        45.8%
        97.5%
        1.1%
        H82_S1_L001_R1
        0.1%
        45.0%
        76bp
        76bp
        0%
        0.0M
        H82_S1_L001_R2
        0.1%
        45.0%
        76bp
        76bp
        0%
        0.0M
        H82_S1_L002
        0.0%
        95.5%
        0.1Mb
        0.0M
        45.9%
        98.0%
        0.5%
        H82_S1_L002_R1
        0.0%
        46.0%
        76bp
        76bp
        18%
        0.0M
        H82_S1_L002_R2
        0.0%
        45.0%
        76bp
        76bp
        9%
        0.0M
        H82_S1_L003
        0.0%
        93.8%
        6.9Mb
        0.1M
        45.8%
        97.6%
        1.1%
        H82_S1_L003_R1
        0.1%
        45.0%
        76bp
        76bp
        0%
        0.0M
        H82_S1_L003_R2
        0.1%
        45.0%
        76bp
        76bp
        0%
        0.0M
        H82_S1_L004
        0.0%
        95.0%
        0.3Mb
        0.0M
        46.0%
        97.9%
        0.5%
        H82_S1_L004_R1
        0.0%
        46.0%
        76bp
        76bp
        9%
        0.0M
        H82_S1_L004_R2
        0.0%
        45.0%
        76bp
        76bp
        18%
        0.0M
        H82_merged
        0.0%
        H82_sorted_dedup
        146bp
        146bp

        Mosdepth

        Fast BAM/CRAM depth calculation for WGS, exome, or targeted sequencing.URL: https://github.com/brentp/mosdepthDOI: 10.1093/bioinformatics/btx699

        Cumulative coverage distribution

        Proportion of bases in the reference genome with, at least, a given depth of coverage. Note that for 2 samples, a BED file was provided, so the data was calculated across those regions. For 2 samples, it's calculated across the entire genome length. 2 samples have both global and region reports, and we are showing the data for regions

        For a set of DNA or RNA reads mapped to a reference sequence, such as a genome or transcriptome, the depth of coverage at a given base position is the number of high-quality reads that map to the reference at that position, while the breadth of coverage is the fraction of the reference sequence to which reads have been mapped with at least a given depth of coverage (Sims et al. 2014).

        Defining coverage breadth in terms of coverage depth is useful, because sequencing experiments typically require a specific minimum depth of coverage over the region of interest (Sims et al. 2014), so the extent of the reference sequence that is amenable to analysis is constrained to lie within regions that have sufficient depth. With inadequate sequencing breadth, it can be difficult to distinguish the absence of a biological feature (such as a gene) from a lack of data (Green 2007).

        For increasing coverage depths (1×, 2×, …, N×), coverage breadth is calculated as the percentage of the reference sequence that is covered by at least that number of reads, then plots coverage breadth (y-axis) against coverage depth (x-axis). This plot shows the relationship between sequencing depth and breadth for each read dataset, which can be used to gauge, for example, the likely effect of a minimum depth filter on the fraction of a genome available for analysis.

        Created with MultiQC

        Average coverage per contig

        Average coverage per contig or chromosome

        Created with MultiQC

        XY coverage

        Created with MultiQC

        GATK

        Wide variety of tools with a primary focus on variant discovery and genotyping.URL: https://www.broadinstitute.org/gatkDOI: 10.1101/201178; 10.1002/0471250953.bi1110s43; 10.1038/ng.806; 10.1101/gr.107524.110

        Observed Quality Scores

        This plot shows the distribution of base quality scores in each sample before and after base quality score recalibration (BQSR). Applying BQSR should broaden the distribution of base quality scores.

        For more information see the Broad's description of BQSR.

        Created with MultiQC

        Reported Quality vs. Empirical Quality

        Plot shows the reported quality score vs the empirical quality score.

        Created with MultiQC

        Picard

        Tools for manipulating high-throughput sequencing data.URL: http://broadinstitute.github.io/picard

        Alignment Summary

        Please note that Picard's read counts are divided by two for paired-end data. Total bases (including unaligned) is not provided.

        Created with MultiQC

        Mean read length

        The mean read length of the set of reads examined.

        Created with MultiQC

        Insert Size

        Plot shows the number of reads at a given insert size. Reads with different orientations are summed.

        Created with MultiQC

        Mark Duplicates

        Number of reads, categorised by duplication state. Pair counts are doubled - see help text for details.

        The table in the Picard metrics file contains some columns referring read pairs and some referring to single reads.

        To make the numbers in this plot sum correctly, values referring to pairs are doubled according to the scheme below:

        • READS_IN_DUPLICATE_PAIRS = 2 * READ_PAIR_DUPLICATES
        • READS_IN_UNIQUE_PAIRS = 2 * (READ_PAIRS_EXAMINED - READ_PAIR_DUPLICATES)
        • READS_IN_UNIQUE_UNPAIRED = UNPAIRED_READS_EXAMINED - UNPAIRED_READ_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_OPTICAL = 2 * READ_PAIR_OPTICAL_DUPLICATES
        • READS_IN_DUPLICATE_PAIRS_NONOPTICAL = READS_IN_DUPLICATE_PAIRS - READS_IN_DUPLICATE_PAIRS_OPTICAL
        • READS_IN_DUPLICATE_UNPAIRED = UNPAIRED_READ_DUPLICATES
        • READS_UNMAPPED = UNMAPPED_READS
        Created with MultiQC

        Mark Illumina Adapters

        Number of Clipped Bases by Read. See the Picard Docuementation for details.

        Created with MultiQC

        fastp

        Version: 0.23.4

        All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560

        Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.

        Filtered Reads

        Filtering statistics of sampled reads.

        Created with MultiQC

        Insert Sizes

        Insert size estimation of sampled reads.

        Created with MultiQC

        Sequence Quality

        Average sequencing quality over each base of all reads.

        Created with MultiQC

        GC Content

        Average GC content over each base of all reads.

        Created with MultiQC

        N content

        Average N content over each base of all reads.

        Created with MultiQC

        FastQC

        Version: 0.12.1

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        Created with MultiQC

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 0/20 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        TTCAGTCTGAAGGGAAAATTGGTAATCAAGTTGAGAAGTCTATCAACAAT
        1
        1
        0.0002%
        TGGCTACATGGTATGACAAACACGTGAAGAAATGTACTGGCTCAATGGAG
        1
        1
        0.0002%
        TGCAGAATAAGGGAAATATACTGGTTACTCCCTTACAAGGCCCATGCACT
        1
        1
        0.0002%
        CACACATCAGCCGCCGCGCTTCCGGGAAGCCACCCATCAGCCGGAGGAGG
        1
        1
        0.0002%
        ACACAGTCTTGATGTATTACCTTTTATTTTTTAAAAAATTTTTATAAAGA
        1
        1
        0.0002%
        AACGAAGGACACAAAGAGGTCCATATATCCACTTGCAGAATTTACAAGGA
        1
        1
        0.0002%
        GGCAGAAGTTGCAGTGAGTTGTAATTGTGCGACTGCACTCCAGCCTCGGC
        1
        1
        0.0002%
        AGGAATGGACCCATTTTTTTCTGCCACTGAAGGACCCACTCATACAACAA
        1
        1
        0.0002%
        GGAAAAAAATAGATCAGATAATCATGGGTGCTATGCAGGAAAATAAAGGT
        1
        1
        0.0002%
        ACTTGAAACATTCGAATGATGAAATATTGAAGTATCTGGTAGTTCTCCAG
        1
        1
        0.0002%
        AAATTGCAGACTGCAGCGTTCTGAGAAACATCTTTGTGATGTTTGTATTC
        1
        1
        0.0002%
        GAAAAAGTTTGCAATCTAGTCATCTGACAAAGGGCTAATATCCAGAATCT
        1
        1
        0.0002%
        TAAATTTATCAGCATCATTAGATATTACCACAGGAAGATTTGTTTTGACT
        1
        1
        0.0002%
        AACAAATTGTACTCTCAGAAAGGTAATTTAGTCACTGATCATCAAAAATG
        1
        1
        0.0002%
        TTTTTGTTTTTTTACATTGCTATTGTCCTGGGAAACCTCTTGATAGTGGT
        1
        1
        0.0002%
        CCTGTAAGCTAATATTTTGCTTTGCTTTTCCCAGAATAAATGTCACTAAA
        1
        1
        0.0002%
        CTACCTCACAATCACAGGGTGCTCTCCAGGGCAGAGGGAGCTCACGTTCT
        1
        1
        0.0002%
        TAAATATGTTTTTAGACACAATGATGGCTGACCCTCCTCCCCAGTGCCTT
        1
        1
        0.0002%
        TACCCTAAAGTGTACCAAGTGATGACAGTTTCTCAGGCAGCACCATCCCT
        1
        1
        0.0002%
        CATTCCAGTCGCCTAATTTCAGTAAGACCTTTGGCAAGTAAGTACCACAA
        1
        1
        0.0002%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        SoftwareVersion
        FastQC0.12.1
        fastp0.23.4